About

Most gridding methods in Verde use a Green’s functions approach.
A linear model is estimated based on the input data and then used to predict
data on a regular grid (or in a scatter, a profile, as derivatives).
The models are Green’s functions from (mostly) elastic deformation theory.
This approach is very similar to machine learning so we implement gridder
classes that are similar to scikit-learn
regression classes.
The API is not 100% compatible but it should look familiar to those with some
scikit-learn experience.

Advantages of using Green’s functions include:

Easily apply weights to data points. This is a linear least-squares
problem.

Perform model selection using established machine learning techniques,
like k-fold or holdout cross-validation.

The estimated model can be easily stored for later use, like
spherical-harmonic coefficients are used in gravimetry.

Contributing Guidelines

Imposter syndrome disclaimer

We want your help. No, really.

There may be a little voice inside your head that is telling you that you’re
not ready to be an open source contributor; that your skills aren’t nearly good
enough to contribute.
What could you possibly offer?

We assure you that the little voice in your head is wrong.

Being a contributor doesn’t just mean writing code.
Equality important contributions include:
writing or proof-reading documentation, suggesting or implementing tests, or
even giving feedback about the project (including giving feedback about the
contribution process).
If you’re coming to the project with fresh eyes, you might see the errors and
assumptions that seasoned contributors have glossed over.
If you can write any code at all, you can contribute code to open source.
We are constantly trying out new skills, making mistakes, and learning from
those mistakes.
That’s how we all improve and we are happy to help others learn.